A Systematic Review of Ten Years of Research on Human Interaction with Social Robots

Lambert, Alexis; Norouzi, Nahal; Bruder, Gerd; Welch, Greg

doi:10.1080/10447318.2020.1801172

Cited by 46 publications

(29 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Looking at trends in human-robot interventions, there are three factors to consider: (i) sample size, which accounts for the number of end-users trialled; we consider the following: limited (1-5), small (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), medium (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), and large (40+); (ii) study duration, which can either account for the total number of HRI sessions or the length of continuous interactions over days, weeks, months or years; (iii) type of setting: controlled (e.g., laboratories, simulated home environments) or real-world (e.g., homes, clinical/care facilities, nursing homes).…”

Section: ) Human-robot Trials and Interventionsmentioning

confidence: 99%

“…Socially assistive robots are well-documented for promise to support ageing and dementia (see [11][12][13][14][15][16][17] for recent reviews). A range of intelligent social robotic platforms from mechanically complex [18,19], mobile [20,21], embodied humanoid SAR [22,23], to pet-like robots [24,25], simpler virtual assistants [26], and commercial smart home speakers (e.g., Amazon Echo, Google Home) have been introduced with strong potential to address the aforementioned needs.…”

Section: Introductionmentioning

confidence: 99%

“…Literature has argued social robots can help improve PwD's social engagement [27], attention [19], neuropsychiatric symptoms through cognitive stimulation [28]; reduce agitation, stress [27] and depressive symptoms [23]; increase cortical neuron activity [29]; and establish rapport with end-users [30]. Yet, long-term user compliance, deployment in the wild, and clinical translation remain largely untapped; development of appropriate robots for dementia is at an early stage with fewer examples beyond research studies [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Conversational Affective Social Robots for Ageing and Dementia Support

Lima

Wairagkar

Gupta³

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation.

show abstract

Section: ) Human-robot Trials and Interventionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conversational Affective Social Robots for Ageing and Dementia Support

Lima

Wairagkar

Gupta³

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

show abstract

“…The proposed approach does not require heavy computing, therefore, it is suitable for use on commercially available robots. We employ this system on Pepper and NAO 7 robots, which are amongst the most commonly used robots in HRI research [53], for our experiments (as described in Section 8). These robots are operated by NAOqi 8 software, which includes different modules that allowed us to extract face similarity scores, gender, height, and age estimations from a single image through the Recognition Module in Figure 13 (Appendix A).…”

Section: Recognition Modulementioning

confidence: 99%

Multi-modal Open World User Identification

Irfan

Ortiz

Lyubova

et al. 2021

J. Hum.-Robot Interact.

View full text Add to dashboard Cite

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.

show abstract

“…Apart from that, for a robot to interact with others, react to them and socially communicate, it needs to be aware of its actions. Consequently, since robots with social skills are incrementally employed in a great variety of applications-such as healthcare [1,2], companionship [3], education [4], and entertainment [5]-there is a need to design more advanced perception systems for them.…”

Section: Introductionmentioning

confidence: 99%

Visual Robotic Perception System with Incremental Learning for Child–Robot Interaction Scenarios

et al. 2021

View full text Add to dashboard Cite

This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. This IL system enables the tutor aspiring to use robotic agents in interaction scenarios to further customize the system according to children’s needs. We perform extensive evaluations of the developed modules, achieving state-of-the-art results on both the children’s action BabyRobot dataset and the children’s emotion EmoReact dataset. Finally, we demonstrate the robustness and effectiveness of the IL system for action recognition by conducting a thorough experimental analysis for various conditions and parameters.

show abstract

A Systematic Review of Ten Years of Research on Human Interaction with Social Robots

Cited by 46 publications

References 104 publications

Conversational Affective Social Robots for Ageing and Dementia Support

Conversational Affective Social Robots for Ageing and Dementia Support

Multi-modal Open World User Identification

Visual Robotic Perception System with Incremental Learning for Child–Robot Interaction Scenarios

Contact Info

Product

Resources

About